HIV Updated - 2021Jan14 Post Meeting Data
Updated Tweet Descriptors
## [1] "total tweets (English-only)"## [1] 69197## [1] "original non-retweets (raw count):"## [1] 25031## [1] "retweet counts (raw count):"## [1] 44166## [1] "tweets that are replies:"## [1] 1328## [1] "tweets that quote (retweet + added own text on top) tweets:"## [1] 4079Updated plot (and some specific numbers to reference)
## # A tibble: 6 x 3
## year tweets accounts
## <dbl> <int> <int>
## 1 2014 2251 1097
## 2 2015 3209 1215
## 3 2016 28254 13109
## 4 2017 10811 5188
## 5 2018 11701 4552
## 6 2019 12971 5592Alternative to graph above if we want a labeled graph
Updated Yearly Proportion Figure (and some specific numbers to reference)
## topic subtotal percent_total num_retweets percent_retweets
## 1 Abstinence 137 0.20 71 51.82
## 2 Condom 950 1.37 578 60.84
## 3 PrEP 13164 19.02 6317 47.99
## 4 Circumcision 836 1.21 364 43.54
## 5 PEP 725 1.05 281 38.76
## 6 Harm Reduction 1249 1.80 804 64.37
## 7 Viral Load 2216 3.20 1349 60.88
## 8 Violence Against Women 310 0.45 177 57.10
## 9 EMTCT 168 0.24 85 50.60
## 10 Serosorting 5 0.01 1 20.00## topic subtotal percent_total num_retweets percent_retweets
## 11 Microbicides 224 0.32 172 76.79
## 12 Sex Work 222 0.32 62 27.93
## 13 Testing 6000 8.67 3494 58.23Note: 46580 tweets (including retweets) do not have our themes.
Top 30 words from topicless tweets
Not sure if this help us but see if there are terms we can use to refine our word lists.
## Joining, by = "word"## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 hiv 9697
## 2 world 4869
## 3 people 4682
## 4 access 4097
## 5 aids 3925
## 6 women 3843
## 7 hands 3832
## 8 million 3705
## 9 prevention 3560
## 10 close 3441## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 gap 3430
## 2 action 3429
## 3 girls 3373
## 4 services 3137
## 5 infections 2943
## 6 treatment 2929
## 7 infected 2917
## 8 health 2434
## 9 urgent 2393
## 10 campaign 2250## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 efforts 1953
## 2 day 1939
## 3 sexual 1696
## 4 join 1690
## 5 lives 1555
## 6 adults 1519
## 7 support 1410
## 8 ensure 1318
## 9 key 1312
## 10 leading 1193Updated Plot
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 2.58 Condom
## 2 2015 1.34 Condom
## 3 2016 1.14 Condom
## 4 2017 1.42 Condom
## 5 2018 2.09 Condom
## 6 2019 0.99 Condom
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 13.8 PrEP
## 2 2015 20.2 PrEP
## 3 2016 5.29 PrEP
## 4 2017 17.6 PrEP
## 5 2018 34.1 PrEP
## 6 2019 37.1 PrEP
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 2.98 Circumcision
## 2 2015 0.62 Circumcision
## 3 2016 0.86 Circumcision
## 4 2017 0.82 Circumcision
## 5 2018 1.34 Circumcision
## 6 2019 2.01 Circumcision
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 0.18 Abstinence
## 2 2015 0.28 Abstinence
## 3 2016 0.28 Abstinence
## 4 2017 0.26 Abstinence
## 5 2018 0.11 Abstinence
## 6 2019 0.03 Abstinence
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 0.31 EMTCT
## 2 2015 0.72 EMTCT
## 3 2016 0.25 EMTCT
## 4 2017 0.21 EMTCT
## 5 2018 0.07 EMTCT
## 6 2019 0.27 EMTCT
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 1.11 PEP
## 2 2015 0.56 PEP
## 3 2016 0.17 PEP
## 4 2017 0.44 PEP
## 5 2018 2.28 PEP
## 6 2019 2.46 PEP
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 8.53 Testing
## 2 2015 8.13 Testing
## 3 2016 6.31 Testing
## 4 2017 7.21 Testing
## 5 2018 11.5 Testing
## 6 2019 12.7 TestingTop Content Generators**
**Based on number of original tweet authorship.
## .
## HIV_Insight Sex_Worker_Hlth DrMbere Hlth_Literacy HIVIreland
## 3144 484 465 444 396
## UNAIDS EPICBrowardOrg Health_HIV2030 HopeAndHelpInc himmoderator
## 269 262 240 221 183
## itech_network one2oneKE GMIPartnership HIVpxresearch Prison_Health
## 170 169 162 152 139
## ProceedNCTSTA UNAIDS_AP AIDSFreeGen AniShakari RectalMicro
## 135 111 110 110 108Top Tweets Based on Retweet Count
## # A tibble: 20 x 4
## retweet_count screen_name date text
## <int> <chr> <date> <chr>
## 1 907 UNAIDS 2016-12-01 "1.9 million adults became infected w…
## 2 493 accphivprn 2019-12-09 "Prevent HIV, you should #BabyYoda #H…
## 3 468 UN 2016-12-01 "The world must take urgent & imm…
## 4 329 UNAIDS 2016-11-30 "Join the #worldaidsday campaign and …
## 5 320 tMSMWellness 2019-03-12 "Trans guy/trans masc and into cis gu…
## 6 274 MissUniverse 2016-12-01 "Know your status. Get Tested. \n\nPo…
## 7 239 ourladyj 2019-06-19 "When folks don’t know their worth, t…
## 8 229 UN 2016-11-30 "#HIVprevention efforts must increase…
## 9 226 ChildtoChild 2016-12-02 "#childparticipation is crucial for e…
## 10 186 UNAIDS 2016-11-22 "More than 2 million people are infec…
## 11 184 AdoreDelano 2016-10-16 "I heart my baes ❤️ #RedDress #LGBTQC…
## 12 173 philredcross 2016-12-01 "#HandsUp for #HIVPrevention. Let's s…
## 13 161 UN_Women 2017-12-01 "There are 870,000 new HIV infections…
## 14 159 UNAIDS 2017-03-08 "New UNAIDS report shows urgent need …
## 15 152 UNAIDS 2016-10-03 "UNAIDS launches #WorldAIDSDay campai…
## 16 149 UNAIDS 2019-03-11 "#HIV was the leading cause of death …
## 17 145 GHDatState 2016-12-01 "Today is #WorldAIDSDay! Together we …
## 18 145 FirstLadyRwa… 2016-12-01 "Together, striving for zero new #HIV…
## 19 141 UNAIDS 2019-10-01 "#HIV was the leading cause of death …
## 20 126 UNFPA 2018-12-01 "Ensuring young people can access com…Top Tweets Based on Times Retweeted
NOTE: Follower count is based on their follower count at the time I collected the data (whenever that is)
## `summarise()` ungrouping output (override with `.groups` argument)## Selecting by retweet_count## # A tibble: 10 x 6
## screen_name retweet_count follower_count ratio tweet_authorship like_count
## <chr> <int> <int> <dbl> <int> <int>
## 1 UNAIDS 11239 258322 0.0435 269 9617
## 2 HIV_Insight 1880 17816 0.106 3144 2459
## 3 MichelSidibe 1551 39187 0.0396 47 1983
## 4 UN 908 12047848 0.0001 5 804
## 5 MissUniverse 705 1022563 0.0007 13 2661
## 6 UNAIDS_AP 687 11923 0.0576 111 652
## 7 HIVpxresearch 499 8778 0.0568 152 750
## 8 accphivprn 493 243 2.03 1 1390
## 9 AniShakari 470 5188 0.0906 110 757
## 10 HIVIreland 468 3564 0.131 396 447## # A tibble: 10 x 6
## screen_name retweet_count follower_count ratio tweet_authorship like_count
## <chr> <int> <int> <dbl> <int> <int>
## 1 GMIPartners… 447 2705 1.65e-1 162 437
## 2 tMSMWellness 443 893 4.96e-1 4 642
## 3 UNFPA 437 214571 2.00e-3 9 552
## 4 kentbuse 408 10892 3.75e-2 69 435
## 5 DrMbere 346 589 5.87e-1 465 71
## 6 UN_Women 323 1792006 2.00e-4 3 281
## 7 frontlineai… 318 23541 1.35e-2 58 344
## 8 philredcross 308 552795 6.00e-4 6 255
## 9 GlobalFund 305 215735 1.40e-3 6 278
## 10 UNESCO 304 3197214 1.00e-4 5 265Map
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